142 research outputs found

    py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets

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    Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full 2D image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields and other sample-dependent properties. However, extracting this information requires complex analysis pipelines, from data wrangling to calibration to analysis to visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail, and present results from several experimental datasets. We have also implemented a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open source HDF5 standard. We hope this tool will benefit the research community, helps to move the developing standards for data and computational methods in electron microscopy, and invite the community to contribute to this ongoing, fully open-source project

    Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

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    We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies

    Evolutionary potential and adaptation of Banksia attenuata (Proteaceae) to climate and fire regime in southwestern Australia, a global biodiversity hotspot

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    Substantial climate changes are evident across Australia, with declining rainfall and rising temperature in conjunction with frequent fires. Considerable species loss and range contractions have been predicted; however, our understanding of how genetic variation may promote adaptation in response to climate change remains uncertain. Here we characterized candidate genes associated with rainfall gradients, temperatures, and fire intervals through environmental association analysis. We found that overall population adaptive genetic variation was significantly affected by shortened fire intervals, whereas declining rainfall and rising temperature did not have a detectable influence. Candidate SNPs associated with rainfall and high temperature were diverse, whereas SNPs associated with specific fire intervals were mainly fixed in one allele. Gene annotation further revealed four genes with functions in stress tolerance, the regulation of stomatal opening and closure, energy use, and morphogenesis with adaptation to climate and fire intervals. B. attenuata may tolerate further changes in rainfall and temperature through evolutionary adaptations based on their adaptive genetic variation. However, the capacity to survive future climate change may be compromised by changes in the fire regime

    The study of atmospheric ice-nucleating particles via microfluidically generated droplets

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    Ice-nucleating particles (INPs) play a significant role in the climate and hydrological cycle by triggering ice formation in supercooled clouds, thereby causing precipitation and affecting cloud lifetimes and their radiative properties. However, despite their importance, INP often comprise only 1 in 10³–10⁶ ambient particles, making it difficult to ascertain and predict their type, source, and concentration. The typical techniques for quantifying INP concentrations tend to be highly labour-intensive, suffer from poor time resolution, or are limited in sensitivity to low concentrations. Here, we present the application of microfluidic devices to the study of atmospheric INPs via the simple and rapid production of monodisperse droplets and their subsequent freezing on a cold stage. This device offers the potential for the testing of INP concentrations in aqueous samples with high sensitivity and high counting statistics. Various INPs were tested for validation of the platform, including mineral dust and biological species, with results compared to literature values. We also describe a methodology for sampling atmospheric aerosol in a manner that minimises sampling biases and which is compatible with the microfluidic device. We present results for INP concentrations in air sampled during two field campaigns: (1) from a rural location in the UK and (2) during the UK’s annual Bonfire Night festival. These initial results will provide a route for deployment of the microfluidic platform for the study and quantification of INPs in upcoming field campaigns around the globe, while providing a benchmark for future lab-on-a-chip-based INP studies

    Spatial replication and habitat context matters for assessments of tropical biodiversity using acoustic indices

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    Approaches to characterise and monitor biodiversity based on the sound signals of ecosystems have become popular in landscape ecology and biodiversity conservation. However, to date, validation studies of how well acoustic indices reflect observed biodiversity patterns have often relied on low levels of either spatial or temporal replication, while focussing on habitats with similar underlying anthropological and geophysical sound characteristics. For acoustic indices to be broadly applicable to biodiversity monitoring, their capacity to measure the ecological facets of soundscapes must be robust to these potential sources of bias. Using two contrasting recording approaches, we examined the efficacy of four commonly used acoustic indices to reflect patterns of observed bird species richness across a tropical forest degradation gradient in Northeast Borneo. The gradient comprised intact and logged forests, riparian forests, remnants, and oil palm plantations, thus providing a highly variable anthrophonic and geophonic soundscape. We compared the degree to which acoustic indices derived from automated versus point count recording methods detected variation in inter-habitat species richness, as well as their capacity to capture changes in species diversity as a consequence of forest degradation quantified by high-resolution LiDAR derived forest canopy heights. We found Acoustic Diversity Index was associated with forest canopy height as measured by both automated recorders and recordings from point counts, whereas the association between canopy height and Acoustic Complexity Index was only detected using point count recordings. For both types of recordings, Acoustic Complexity Index exhibited the strongest relationship with observed bird richness in old growth and logged forest, whereas Acoustic Diversity was not linked, suggesting avian richness does not drive its association with canopy height. No acoustic indices were associated with observed bird richness in oil palm riparian areas. Our findings underscore the potential utility of soundscape approaches to characterise biodiversity patterns in degraded tropical landscapes, and may be used as a proxy for human inventories of bird communities. However, we also show that for acoustic indices to be effective on landscape-wide studies of environmental gradients, adequate spatial replication is required, and care must be taken to control for non-target elements of soundscapes in different habitats
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